1. Field of the Invention
The present invention relates generally to an image rendering method and an image rendering apparatus. The invention relates to, for example, anisotropic texture mapping.
2. Description of the Related Art
In recent years, the performance of image rendering apparatus has remarkably been enhanced.
Digital image data is represented by a set of pixels. When a pixel process is executed, an image (texture) that represents a picture/pattern or material properties of a surface of an object is added. Thereby, a more detailed representation is realized. This process is called “texture mapping”. The position of each pixel on the texture can be understood from an image (pixel footprint) that is obtained by projecting the pixel back onto the texture. In some cases, the footprint is distorted with anisotropy on the texture. In such cases, the direction of anisotropy is estimated, and the texture is reconstructed with some filtering, as disclosed, for instance, in Paul S. Heckbert, “Fundamentals of Texture Mapping and Image Warping (Masters Thesis)”, Report No. UCB/CSD 89/516, Computer Science Division, University of California, Berkeley, June 1989.
In this method, however, a normal distribution (Gaussian) which centers at a pixel is considered on a frame buffer. The normal distribution is projected back onto the texture, and an elliptically distorted filter (elliptical weighted average) is applied. Consequently, an enormous amount of calculation is required, and the cost for calculation is high. This is not suited to hardware implementation.
Another method is disclosed in the home page of Open GL Extension (oss.sgi.com/projects/ogl-sample/registry/EXT/texture_filter_anisotropic.txt). In this method, the cost for calculation can be reduced by using a MIP mapping circuit multiple times. However, as the shape of the footprint of a pixel becomes less similar to a rectangle, the calculation of anisotropy tends to become more difficult. As a result, the image blurs and the precision of image rendering deteriorates.